NIH RFA-OD-09-003 This application addresses broad Challenge Area (06) Enabling Technologies, and specific Challenge Topic, 06- LM-102: Self-documenting encounters. Narrative data account for much of the information that is documented in patient encounters. With the advances in EHRs, both in menu-based and speech/writing recognition-based systems, it is vital to identify which relevant language characteristics need to be captured in documenting clinical encounters. At present, it is unknown whether language characteristics other than medical keywords used in menu-based systems help to improve the quality of chart notes. The current project analyzes over 1500 chart notes collected over the last six years, whereby each chart note has been graded by two MD faculty on five dimensions. Four computational linguistic models addressing general linguistic features, cohesion and readability, personality and psychological features, and subjectivity of text will analyze these chart notes in order to determine which language characteristics best explain the different grades. These findings will be used to compare original chart notes with notes created using existing EHRs, to determine the extent to which EHRs might benefit from augmented language characteristics. Knowing which language characteristics are essential in documenting clinical encounters is informative for emerging technologies, but knowing whether existing EHRs can benefit from adding these characteristics is an additional urgent question. Using OpenSource code for EHRs, language characteristics that prove to be important for the quality of chart notes will be implemented into the OpenSource EHR. An experiment using four standardized patient cases will evaluate the benefits and drawbacks of an extended EHR with regard to informativeness and usability. The findings of this project could have an enormous impact on the development of EHRs. Knowing which language characteristics are important in clinical encounters is informative for existing technologies and emerging technologies alike. The findings could bootstrap the development of speech recognition and handwriting recognition EHRs, give a new stimulus to menu-based EHRs, and have a significant impact on the quality of future chart notes, and subsequently on the diagnosis and treatment based on these notes. As the health care system is in immediate need of transitioning to electronic health records, it is essential to get the best quality and most intuitive technologies from the onset. Because chart notes are very much based on language, knowing which language characteristics constitute high-quality chart notes is vital for diagnosis and treatment, which rely on these notes. Not knowing which specific characteristics distinguish high-quality from low-quality EHRs could negatively impact millions of Americans and cost tens of millions of dollars.